






import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 		_snake_case	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     Optional[int]        =				AudioLDMPipeline
			_lowercase		:     Union[str, Any]        =				TEXT_TO_AUDIO_PARAMS
			_lowercase		:     List[str]        =				TEXT_TO_AUDIO_BATCH_PARAMS
			_lowercase		:     Tuple        =				frozenset(
			    [
			        '''num_inference_steps''',
			        '''num_waveforms_per_prompt''',
			        '''generator''',
			        '''latents''',
			        '''output_type''',
			        '''return_dict''',
			        '''callback''',
			        '''callback_steps''',
			    ]       )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			UNetaDConditionModel(
						    block_out_channels=(32, 64)		,       layers_per_block=2		,       sample_size=32		,       in_channels=4		,       out_channels=4		,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,       cross_attention_dim=(32, 64)		,       class_embed_type='simple_projection'		,       projection_class_embeddings_input_dim=32		,       class_embeddings_concat=a		,       )
						SCREAMING_SNAKE_CASE							=			DDIMScheduler(
						    beta_start=0.0_00_85		,       beta_end=0.0_12		,       beta_schedule='scaled_linear'		,       clip_sample=a		,       set_alpha_to_one=a		,       )
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			AutoencoderKL(
						    block_out_channels=[32, 64]		,       in_channels=1		,       out_channels=1		,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,       latent_channels=4		,       )
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			ClapTextConfig(
						    bos_token_id=0		,       eos_token_id=2		,       hidden_size=32		,       intermediate_size=37		,       layer_norm_eps=1E-05		,       num_attention_heads=4		,       num_hidden_layers=5		,       pad_token_id=1		,       vocab_size=1000		,       projection_dim=32		,       )
						SCREAMING_SNAKE_CASE							=			ClapTextModelWithProjection(a)
						SCREAMING_SNAKE_CASE							=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'		,       model_max_length=77)

						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGanConfig(
						    model_in_dim=8		,       sampling_rate=1_6000		,       upsample_initial_channel=16		,       upsample_rates=[2, 2]		,       upsample_kernel_sizes=[4, 4]		,       resblock_kernel_sizes=[3, 7]		,       resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]		,       normalize_before=a		,       )

						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGan(a)

						SCREAMING_SNAKE_CASE							=			{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'vocoder': vocoder,
						}
						return components






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a=0)   ->							int:
						if str(a).startswith('mps'):
									SCREAMING_SNAKE_CASE							=			torch.manual_seed(a)
						else:
									SCREAMING_SNAKE_CASE							=			torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE							=			{
						    'prompt': 'A hammer hitting a wooden surface',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						}
						return inputs






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator

						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) == 256

						SCREAMING_SNAKE_CASE							=			audio[:10]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33])

						assert np.abs(audio_slice - expected_slice).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs['prompt']]

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs.pop('prompt')]

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.tokenizer(
						    a		,       padding='max_length'		,       max_length=audioldm_pipe.tokenizer.model_max_length		,       truncation=a		,       return_tensors='pt'		,       )
						SCREAMING_SNAKE_CASE							=			text_inputs['input_ids'].to(a)

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.text_encoder(
						    a		,       )
						SCREAMING_SNAKE_CASE							=			prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						SCREAMING_SNAKE_CASE							=			F.normalize(a		,       dim=-1)

						SCREAMING_SNAKE_CASE							=			prompt_embeds

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert np.abs(audio_a - audio_a).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * ['this is a negative prompt']
						SCREAMING_SNAKE_CASE							=			negative_prompt
						SCREAMING_SNAKE_CASE							=			3 * [inputs['prompt']]

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs.pop('prompt')]

						SCREAMING_SNAKE_CASE							=			[]
						for p in [prompt, negative_prompt]:
									SCREAMING_SNAKE_CASE							=			audioldm_pipe.tokenizer(
									    a		,       padding='max_length'		,       max_length=audioldm_pipe.tokenizer.model_max_length		,       truncation=a		,       return_tensors='pt'		,       )
									SCREAMING_SNAKE_CASE							=			text_inputs['input_ids'].to(a)

									SCREAMING_SNAKE_CASE							=			audioldm_pipe.text_encoder(
									    a		,       )
									SCREAMING_SNAKE_CASE							=			text_embeds.text_embeds
									# additional L_2 normalization over each hidden-state
									SCREAMING_SNAKE_CASE							=			F.normalize(a		,       dim=-1)

									embeds.append(a)

						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			embeds

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert np.abs(audio_a - audio_a).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			PNDMScheduler(skip_prk_steps=a)
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			'egg cracking'
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a		,       negative_prompt=a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) == 256

						SCREAMING_SNAKE_CASE							=			audio[:10]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32])

						assert np.abs(audio_slice - expected_slice).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			PNDMScheduler(skip_prk_steps=a)
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			'A hammer hitting a wooden surface'

						# test num_waveforms_per_prompt=1 (default)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=2).audios

						assert audios.shape == (1, 256)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe([prompt] * batch_size		,       num_inference_steps=2).audios

						assert audios.shape == (batch_size, 256)

						# test num_waveforms_per_prompt for single prompt
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=2		,       num_waveforms_per_prompt=a).audios

						assert audios.shape == (num_waveforms_per_prompt, 256)

						# test num_waveforms_per_prompt for batch of prompts
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(
						    [prompt] * batch_size		,       num_inference_steps=2		,       num_waveforms_per_prompt=a).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.vocoder.config.sampling_rate

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(audio_length_in_s=0.0_16		,       **a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) / vocoder_sampling_rate == 0.0_16

						SCREAMING_SNAKE_CASE							=			audioldm_pipe(audio_length_in_s=0.0_32		,       **a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) / vocoder_sampling_rate == 0.0_32






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			['hey']

						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=1)
						SCREAMING_SNAKE_CASE							=			output.audios.shape
						assert audio_shape == (1, 256)

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGan(a).to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=1)
						SCREAMING_SNAKE_CASE							=			output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 256)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						self._test_inference_batch_single_identical(test_mean_pixel_difference=a)







			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()		,       reason='XFormers attention is only available with CUDA and `xformers` installed'		,       )
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a)



@slow
class 		_snake_case	(	unittest.TestCase       ):






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a="cpu"		,       a=torch.floataa		,       a=0)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE							=			np.random.RandomState(a).standard_normal((1, 8, 128, 16))
						SCREAMING_SNAKE_CASE							=			torch.from_numpy(a).to(device=a		,       dtype=a)
						SCREAMING_SNAKE_CASE							=			{
						    'prompt': 'A hammer hitting a wooden surface',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 2.5,
						}
						return inputs






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline.from_pretrained('cvssp/audioldm')
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_inputs(a)
						SCREAMING_SNAKE_CASE							=			25
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a).audios[0]

						assert audio.ndim == 1
						assert len(a) == 8_1920

						SCREAMING_SNAKE_CASE							=			audio[7_7230:7_7240]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15])
						SCREAMING_SNAKE_CASE							=			np.abs(expected_slice - audio_slice).max()
						assert max_diff < 1E-2







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline.from_pretrained('cvssp/audioldm')
						SCREAMING_SNAKE_CASE							=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a).audios[0]

						assert audio.ndim == 1
						assert len(a) == 8_1920

						SCREAMING_SNAKE_CASE							=			audio[2_7780:2_7790]
						SCREAMING_SNAKE_CASE							=			np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12])
						SCREAMING_SNAKE_CASE							=			np.abs(expected_slice - audio_slice).max()
						assert max_diff < 3E-2





'''simple docstring'''




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__UpperCAmelCase					={
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

__UpperCAmelCase					={
    "ctrl": 2_5_6,
}

__UpperCAmelCase					={
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    set()
    __lowerCamelCase            =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)							)
        __lowerCamelCase            =    char

    __lowerCamelCase            =    set(UpperCamelCase__							)
    return pairs




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	str				=VOCAB_FILES_NAMES
     lowerCamelCase    :	Optional[int]				=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase    :	Tuple				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase    :	Union[str, Any]				=CONTROL_CODES






     def __init__(					self						:						Optional[Any]    ,							a						:						str    ,							a						:						List[str]    ,							a						:						Optional[int]="<unk>"    ,							**a						:						Any       ):





         """simple docstring"""



         super().__init__(unk_token=a    ,							**a       )

         with open(a    ,							encoding='''utf-8'''       ) as vocab_handle:
             __lowerCamelCase            =    json.load(a       )
         __lowerCamelCase            =    {v: k for k, v in self.encoder.items()}
         with open(a    ,							encoding='''utf-8'''       ) as merges_handle:
             __lowerCamelCase            =    merges_handle.read().split('''\n'''       )[1:-1]
         __lowerCamelCase            =    [tuple(merge.split()       ) for merge in merges]
         __lowerCamelCase            =    dict(zip(a    ,							range(len(a       )       )       )       )
         __lowerCamelCase            =    {}






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         return len(self.encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         return dict(self.encoder    ,							**self.added_tokens_encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         if token in self.cache:
             return self.cache[token]
         __lowerCamelCase            =    tuple(a       )
         __lowerCamelCase            =    tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
         __lowerCamelCase            =    get_pairs(a       )

         if not pairs:
             return token

         while True:
             __lowerCamelCase            =    min(a    ,							key=lambda a       : self.bpe_ranks.get(a    ,							float('''inf'''       )       )       )
             if bigram not in self.bpe_ranks:
                 break
             __lowerCamelCase      ,    __lowerCamelCase            =    bigram
             __lowerCamelCase            =    []
             __lowerCamelCase            =    0
             while i < len(a       ):
                 try:
                     __lowerCamelCase            =    word.index(a    ,							a       )
                 except ValueError:
                     new_word.extend(word[i:]       )
                     break
                 else:
                     new_word.extend(word[i:j]       )
                     __lowerCamelCase            =    j

                 if word[i] == first and i < len(a       ) - 1 and word[i + 1] == second:
                     new_word.append(first + second       )
                     i += 2
                 else:
                     new_word.append(word[i]       )
                     i += 1
             __lowerCamelCase            =    tuple(a       )
             __lowerCamelCase            =    new_word
             if len(a       ) == 1:
                 break
             else:
                 __lowerCamelCase            =    get_pairs(a       )
         __lowerCamelCase            =    '''@@ '''.join(a       )
         __lowerCamelCase            =    word[:-4]
         __lowerCamelCase            =    word
         return word






     def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    []

         __lowerCamelCase            =    re.findall(R'''\S+\n?'''    ,							a       )

         for token in words:
             split_tokens.extend(list(self.bpe(a       ).split(''' '''       )       )       )
         return split_tokens






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Dict       ):





         """simple docstring"""



         return self.encoder.get(a    ,							self.encoder.get(self.unk_token       )       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         return self.decoder.get(a    ,							self.unk_token       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    ''' '''.join(a       ).replace('''@@ '''    ,							''''''       ).strip()
         return out_string






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						str    ,							a						:						Optional[str] = None       ):





         """simple docstring"""



         if not os.path.isdir(a       ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
             return
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as f:
             f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=a    ,							ensure_ascii=a       ) + '''\n'''       )

         __lowerCamelCase            =    0
         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as writer:
             writer.write('''#version: 0.2\n'''       )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda a       : kv[1]       ):
                 if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                         ''' Please check that the tokenizer is not corrupted!'''       )
                     __lowerCamelCase            =    token_index
                 writer.write(''' '''.join(a       ) + '''\n'''       )
                 index += 1

         return vocab_file, merge_file

     # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
     #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
     #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
     #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
     #     return ''.join(tokens_generated_so_far)
